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Installation script for facebookresearch / pifuhd
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#!/bin/bash | |
minVersion="$(python3 -V 2>&1 | grep -Po '(?<=Python 3\.)([0-9]+)')" | |
if (( "$minVersion" <= 5 )); then | |
echo "You're python version is too old. You need at least Python 3.6.x" >&2 | |
echo "Try the following: " | |
echo " sudo add-apt-repository ppa:deadsnakes/ppa" | |
echo " sudo apt update" | |
echo " sudo apt upgrade" | |
echo " sudo apt install python3.8 python3.8-venv python3.8-dev python3.8-gdbm" | |
echo "Then make sure that Python 3.8 is the default interpreter by either configuring update-alternatives or using an alias in your .bashrc." | |
echo "You can find more information here: https://medium.com/analytics-vidhya/installing-python-3-8-3-66701d3db134" | |
exit 1 | |
fi | |
installPath="$(pwd)" | |
if [ -n "$1" ]; then | |
mkdir -p "$1" || exit 1 | |
installPath="(realpath "$1")" | |
fi | |
extraPackages=( | |
"liboctomap-dev" | |
"libfcl-dev" | |
"libspatialindex-dev" | |
"libgl1-mesa-glx" | |
) | |
installPackages=() | |
for package in "${extraPackages[@]}"; do | |
if ! dpkg-query -W -f='${Status}\n' "$package" 2>/dev/null | grep -q "install ok"; then | |
installPackages+=("$package") | |
fi | |
done | |
if (( "${#installPackages[@]}" > 0 )); then | |
echo "The following additional packages are needed for trimesh[all], rtree and opencv2: ${installPackages[@]}" | |
sudo apt install "${installPackages[@]}" || exit 1 | |
fi | |
versionError=false | |
fclVersion="$(dpkg-query -W -f='${Version}\n' libfcl-dev)" | |
if ! [[ "$fclVersion" =~ ^0\.5.* ]]; then | |
echo "libfcl-dev is only available in version $fclVersion but we need at least version 0.5.0." >&2 | |
versionError=true | |
fi | |
octoVersion="$(dpkg-query -W -f='${Version}\n' liboctomap-dev)" | |
if ! [[ "$octoVersion" =~ ^1\.[89].* ]]; then | |
echo "liboctomap-dev is only available in version $octoVersion but we need at least version 1.8.0." >&2 | |
versionError=true | |
fi | |
if $versionError; then | |
echo "You can install the following packages manually and restart the script:" | |
echo " http://mirrors.kernel.org/ubuntu/pool/universe/f/fcl/libfcl0.5_0.5.0-5_amd64.deb" | |
echo " http://mirrors.kernel.org/ubuntu/pool/universe/f/fcl/libfcl-dev_0.5.0-5_amd64.deb" | |
echo " http://mirrors.kernel.org/ubuntu/pool/universe/o/octomap/liboctomap1.8_1.8.1+dfsg-1_amd64.deb" | |
echo " http://mirrors.kernel.org/ubuntu/pool/universe/o/octomap/liboctomap-dev_1.8.1+dfsg-1_amd64.deb" | |
echo "" | |
echo "Example:" | |
echo " wget http://mirrors.kernel.org/ubuntu/pool/universe/f/fcl/libfcl0.5_0.5.0-5_amd64.deb" | |
echo " sudo dpkg -i libfcl0.5_0.5.0-5_amd64.deb" | |
exit 1 | |
fi | |
cd "$installPath" | |
if ! [ -d "pifuhd" ]; then | |
git clone https://github.com/facebookresearch/pifuhd.git || exit 1 | |
fi | |
cd pifuhd | |
if ! [ -f "checkpoints/pifuhd.pt" ]; then | |
sh ./scripts/download_trained_model.sh | |
fi | |
if ! [ -d ".venv" ]; then | |
python3 -m venv .venv | |
fi | |
source .venv/bin/activate | |
pip install --upgrade pip || exit 1 | |
pip install numpy || exit 1 | |
pip install torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html || exit 1 | |
pip install wheel || exit 1 | |
pip install opencv-python tqdm matplotlib scikit-image pyopengl trimesh[all] pycocotools || exit 1 | |
mkdir -p content | |
cd content | |
if ! [ -d "lightweight-human-pose-estimation.pytorch" ]; then | |
git clone https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch.git | |
fi | |
cd lightweight-human-pose-estimation.pytorch | |
wget -nc https://download.01.org/opencv/openvino_training_extensions/models/human_pose_estimation/checkpoint_iter_370000.pth | |
cat > "getrect.py" << EOM | |
import torch | |
import cv2 | |
import numpy as np | |
from models.with_mobilenet import PoseEstimationWithMobileNet | |
from modules.keypoints import extract_keypoints, group_keypoints | |
from modules.load_state import load_state | |
from modules.pose import Pose, track_poses | |
import demo | |
import argparse | |
parser = argparse.ArgumentParser() | |
parser.add_argument('-i', '--image_path', type=str, default=None) | |
args = parser.parse_args() | |
def get_rect(net, images, height_size, cpu=False): | |
net = net.eval() | |
stride = 8 | |
upsample_ratio = 4 | |
num_keypoints = Pose.num_kpts | |
previous_poses = [] | |
delay = 33 | |
for image in images: | |
rect_path = image.replace('.%s' % (image.split('.')[-1]), '_rect.txt') | |
img = cv2.imread(image, cv2.IMREAD_COLOR) | |
orig_img = img.copy() | |
orig_img = img.copy() | |
heatmaps, pafs, scale, pad = demo.infer_fast(net, img, height_size, stride, upsample_ratio, cpu=cpu) | |
total_keypoints_num = 0 | |
all_keypoints_by_type = [] | |
for kpt_idx in range(num_keypoints): # 19th for bg | |
total_keypoints_num += extract_keypoints(heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num) | |
pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, pafs, demo=True) | |
for kpt_id in range(all_keypoints.shape[0]): | |
all_keypoints[kpt_id, 0] = (all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale | |
all_keypoints[kpt_id, 1] = (all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale | |
current_poses = [] | |
rects = [] | |
for n in range(len(pose_entries)): | |
if len(pose_entries[n]) == 0: | |
continue | |
pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1 | |
valid_keypoints = [] | |
for kpt_id in range(num_keypoints): | |
if pose_entries[n][kpt_id] != -1.0: # keypoint was found | |
pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0]) | |
pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1]) | |
valid_keypoints.append([pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]]) | |
valid_keypoints = np.array(valid_keypoints) | |
if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0: | |
pmin = valid_keypoints.min(0) | |
pmax = valid_keypoints.max(0) | |
center = (0.5 * (pmax[:2] + pmin[:2])).astype(np.int) | |
radius = int(0.65 * max(pmax[0]-pmin[0], pmax[1]-pmin[1])) | |
elif pose_entries[n][10] == -1.0 and pose_entries[n][13] == -1.0 and pose_entries[n][8] != -1.0 and pose_entries[n][11] != -1.0: | |
# if leg is missing, use pelvis to get cropping | |
center = (0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype(np.int) | |
radius = int(1.45*np.sqrt(((center[None,:] - valid_keypoints)**2).sum(1)).max(0)) | |
center[1] += int(0.05*radius) | |
else: | |
center = np.array([img.shape[1]//2,img.shape[0]//2]) | |
radius = max(img.shape[1]//2,img.shape[0]//2) | |
x1 = center[0] - radius | |
y1 = center[1] - radius | |
rects.append([x1, y1, 2*radius, 2*radius]) | |
np.savetxt(rect_path, np.array(rects), fmt='%d') | |
net = PoseEstimationWithMobileNet() | |
checkpoint = torch.load('checkpoint_iter_370000.pth', map_location=torch.device('cpu')) | |
load_state(net, checkpoint) | |
get_rect(net.cpu(), [args.image_path], 512, cpu = True) | |
EOM | |
cd "${installPath}/pifuhd" | |
git apply - 2>/dev/null << EOM | |
diff --git a/apps/recon.py b/apps/recon.py | |
index 8b2c98f..a5f75f3 100644 | |
--- a/apps/recon.py | |
+++ b/apps/recon.py | |
@@ -145,7 +145,7 @@ def recon(opt, use_rect=False): | |
state_dict = None | |
if state_dict_path is not None and os.path.exists(state_dict_path): | |
print('Resuming from ', state_dict_path) | |
- state_dict = torch.load(state_dict_path) | |
+ state_dict = torch.load(state_dict_path, map_location=torch.device('cpu')) | |
print('Warning: opt is overwritten.') | |
dataroot = opt.dataroot | |
resolution = opt.resolution | |
@@ -162,7 +162,7 @@ def recon(opt, use_rect=False): | |
# parser.print_options(opt) | |
- cuda = torch.device('cuda:%d' % opt.gpu_id) | |
+ cuda = torch.device('cpu') | |
if use_rect: | |
test_dataset = EvalDataset(opt) | |
EOM | |
cat >> ".gitignore" << EOM | |
content/ | |
.vscode/ | |
.venv/ | |
*.pyc | |
results/ | |
checkpoints/ | |
EOM | |
cat > "run.sh" << EOM | |
#!/bin/bash | |
if [ -z "\$1" ]; then | |
echo "Missing parameter: $0 <image>" >&2 | |
exit 1 | |
fi | |
image="\$(realpath "\$1")" | |
dirImage="\$(dirname "\$image")" | |
source .venv/bin/activate | |
cd content/lightweight-human-pose-estimation.pytorch | |
python getrect.py -i "\$image" | |
cd ../.. | |
python -m apps.simple_test -r 256 --use_rect -i "\$dirImage" | |
EOM | |
chmod +x run.sh | |
mv samples_images/test_keypoints.json{,.bak} 2>/dev/null | |
echo "You can now go to ${installPath}/pifuhd/ and execute" | |
echo " run.sh samples_images/test.png" | |
echo "to check if it is working correctly." | |
echo "" | |
echo "Change the value in the last line in 'run.sh' to 512 if you have enough memory." | |
echo "You can find the results in results/pifuhd_final/recon/" |
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Unfortunately I am also not experienced in AI stuff. I was only able to get it installed properly. I didn't use this script since I created it. But it seems that the human pose estimation got an update and the parameter
demo
is no longer used as you can see here: https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch/blob/1590929b601535def07ead5522f05e5096c1b6ac/modules/keypoints.py#L64It was changed already 3 years ago in this commit: Daniil-Osokin/lightweight-human-pose-estimation.pytorch@ee9e4cc
So if you are lucky you can just delete the
demo=True
parameter ingetrect.py
on line 30 and try again.